The goal of this project is to facilitate the development and evaluation of statistical methods for identifying and characterizing the genetic contribution to complex diseases and their precursors and risk factors. We will pursue this goal by continuing the organization of the Genetic Analysis Workshops (GAWs), which began in 1982. The Genetic Analysis Workshops are a collaborative effort among genetic epidemiologists to evaluate and compare statistical genetic methods. For each GAW, topics are chosen for their relevance to current analytical issues in genetic epidemiology, and sets of real and computer-simulated data are distributed to investigators worldwide. Participants submit the results of their analyses, which are discussed and compared at a 3 1/2 day meeting. Participation at GAWs has increased tremendously, from fewer than 30 at GAW1 in 1982 to 345 at GAW15. In the current grant period, the proceedings of GAW13 (held in 2002) were published, and GAW14 (2004) and GAW15 (2006) were held. Preparation of the GAW15 proceedings is in progress and planning for GAW16 has begun. During the proposed grant period, three GAWs will be held: GAW 16 (in 2008), GAW17 (in 2010), and GAW18 (in 2012). [unreadable] [unreadable] Before the Workshops, participants communicate with others who have done similar types of analyses, and plan integrated presentations. The GAW submissions invariably contain new ideas for methods to handle complex phenotypes. Recent GAWs have included genome scan data (microsatellites and SNPs), dense SNP data in specific chromosomal regions, simulated sequence data, as well as microarray expression data, giving participants an opportunity to try out new methods for localizing and characterizing disease-causing genes. For the first time in GAW13, longitudinal phenotypic data were distributed (from the Framingham Heart Study). Topics for future GAWs will be selected from among currently challenging analytical problems. Suggestions include methods for analyzing genotype W environment interaction; how to best use very high density marker maps; following up genome-wide localization with region-specific, gene-centric, and/or sequence-based analyses; analysis of transcriptomic (RNA expression phenotypes) and other types of -omics data; and issues involved in analysis of association-based genome-wide screening. [unreadable] [unreadable] We will continue to distribute real and simulated data from past GAWs with the permission of the data providers, as well as programs for genetic analysis. Long after each GAW is over, investigators continue to use GAW data sets to evaluate new analytical methods and software, to estimate power and false positive rates, and to demonstrate the feasibility of statistical techniques for finding disease genes. GAW data are extensively used in grant proposals and in teaching and dissertation research. [unreadable] [unreadable] [unreadable]